Proliferative and transcriptomic response of experimental B16-F10 melanoma to modulation of murine microbiota by oral administration of Lacticaseibacillus rhamnosus K32 and Bifidobacterium adolescentis 150

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Abstract

BACKGROUND: Probiotics are capable of modulating immune responses through interactions with the gut microbiota, potentially enhancing the efficacy of immunotherapy and reducing adverse effects of chemotherapy and radiotherapy. Certain probiotic strains have demonstrated the ability to suppress chronic inflammation and augment antitumor immunity; however, their clinical application requires further investigation.

AIM: This work aimed to evaluate the effects of oral administration of the probiotic strains Lacticaseibacillus rhamnosus K32 and Bifidobacterium adolescentis 150 on tumor growth and gene expression in the B16-F10 melanoma model, as well as on gut microbiota composition in experimental animals.

METHODS: The experiment was conducted in C57BL/6 mice bearing B16-F10 melanoma. Animals were divided into three groups: control (no intervention) and two experimental groups for oral administration of B. adolescentis 150 or L. rhamnosus K32, respectively. Changes in gut microbiota composition were analyzed by full-length 16S rRNA gene sequencing using Oxford Nanopore technology. The transcriptomic response of B16-F10 melanoma cells to probiotic administration was assessed by RNA sequencing.

RESULTS: Substantial differences were observed in the effects of the studied probiotic strains on B16-F10 melanoma progression. B. adolescentis 150 significantly stimulated experimental tumor growth by 29% (padj. = 0.02 vs. control; padj. = 0.001 vs. L. rhamnosus K32; adj., Bonferroni correction applied). At the molecular level, this stimulation was associated with suppression of interferon signaling, activation of proliferative pathways (WNT/β-catenin, TGF-β), and reduced expression of immune cell markers in melanoma tissue. In contrast, L. rhamnosus K32 reduced tumor growth by 18% (not significant; padj. = 0.4) and was associated with increased expression of cytotoxic T lymphocyte and NK cell markers, as well as activation of interferon response pathways. Both probiotic strains induced marked alterations in gut microbiota composition, characterized by an increased relative abundance of Klebsiella spp., and were associated with activation of proinflammatory signaling pathways (NF-κB, IL-6/JAK/STAT3, IL-2/STAT5) in tumor tissue. Notably, administration of both probiotics was linked to activation of epithelial–mesenchymal transition and hypoxia in the tumor, potentially creating conditions favorable for tumor progression and metastasis.

CONCLUSION: These findings highlight the complex and context-dependent effects of probiotics on tumor development and underscore the need for careful strain selection in the adjuvant therapy of melanoma and other malignancies.

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About the authors

Evgenii I. Olekhnovich

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency

Author for correspondence.
Email: jeniaole13@mail.ru
ORCID iD: 0000-0003-4899-342X
SPIN-code: 4366-8269

Cand. Sci. (Biology)

Russian Federation, Moscow

Alexandra A. Strokach

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency

Email: alexandra.vlasova.2017@yandex.ru
ORCID iD: 0009-0009-9470-7640
SPIN-code: 7963-6910
Russian Federation, Moscow

Vera A. Kanaeva

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency; Moscow Institute of Physics and Technology

Email: vera.a.kanaeva@gmail.com
ORCID iD: 0009-0005-7214-2504
SPIN-code: 3295-3765
Russian Federation, Moscow; Dolgoprudny

Maxim D. Morozov

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency

Email: maxim_d_morozov@mail.ru
ORCID iD: 0000-0001-6128-0921
SPIN-code: 4872-7881
Russian Federation, Moscow

Vladimir A. Veselovsky

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency

Email: djdf26@gmail.com
ORCID iD: 0000-0002-4336-9452
SPIN-code: 4080-4861

Cand. Sci. (Biology)

Russian Federation, Moscow

Polina Yu. Zoruk

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency

Email: z-polly@mail.ru
ORCID iD: 0009-0007-3397-2024
SPIN-code: 4530-7951
Russian Federation, Moscow

Artem B. Ivanov

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency

Email: abivanov@itmo.ru
ORCID iD: 0000-0002-7997-0637

Cand. Sci. (Technology)

Russian Federation, Moscow

Maya V. Odorskaya

Institute of General Genetics of the Russian Academy of Sciences

Email: maya_epifanova@mail.ru
ORCID iD: 0000-0002-9821-9865
Russian Federation, Moscow

Severina D. Koldman

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency; State Scientific Center of the Russian Federation—Federal Medical Biophysical Center named after A.I. Burnazyan

Email: zaianari@mail.ru
ORCID iD: 0000-0002-7496-1213
SPIN-code: 8986-7396

Cand. Sci. (Biology)

Russian Federation, Moscow; Moscow

Vail A. Koldman

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency; State Scientific Center of the Russian Federation—Federal Medical Biophysical Center named after A.I. Burnazyan

Email: ajalein@xmail.ru
ORCID iD: 0000-0001-6601-7700
SPIN-code: 5866-3613

Cand. Sci. (Biology)

Russian Federation, Moscow; Moscow

Ksenia M. Klimina

Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency; Institute of General Genetics of the Russian Academy of Sciences

Email: ppp843@yandex.ru
ORCID iD: 0000-0002-5563-644X
SPIN-code: 8830-4325

Cand. Sci. (Biology)

Russian Federation, Moscow; Moscow

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Supplementary files

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2. Supplement 1. Tumor growth trends in control and experimental groups.
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3. Supplement 2. Quality control of sequencing data from mouse fecal samples.
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4. Supplement 3. Relative abundance of bacterial genera in the murine gut microbiota based on 16S rRNA gene sequencing.
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5. Supplement 4. Differential abundance of taxa at the genus level between experimental groups using different reference comparisons.
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6. Supplement 5. Summary quality control metrics for bulk RNA-seq data from experimental tumors.
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7. Supplement 6. Normalized gene expression data from experimental tumors.
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8. Supplement 7. Gene lists of the Cell Cycle and Extracellular Matrix co-expression clusters.
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9. Supplement 8. Results of over-representation analysis for the Cell Cycle and Extracellular Matrix clusters.
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10. Fig. 1. Tumor growth trends in experimental groups accounting for biological replicates. The plot illustrates changes in tumor area (cm²) over time (days) after inoculation in experimental animals. Individual growth kinetics for each mouse are shown (thin pale lines), grouped by experimental condition (color) and biological replicate. Mean values for each group within each replicate are presented as thick, bright lines. The correspondence between colors, line types, experimental groups, and replicates is indicated in the legend above the plot: melanoma (M), melanoma combined with L. rhamnosus K32 (M_LAC), and melanoma combined with B. adolescentis 150 (M_BIF). Results from three independent experiments are shown for each group (designated as batch_1, batch_2, batch_3).

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11. Fig. 2. Results of differential abundance analysis obtained using the MaAsLin2 algorithm. The X-axis represents the MaAsLin2 regression coefficient (MaAsLin2 coef), and the Y-axis shows the −log10-transformed q-value: a, comparison between M_BIF and M groups (reference); b, comparison between M_LAC and M groups (reference); c, comparison between M_BIF and M_LAC groups (reference).

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12. Fig. 3. Results of differential gene expression analysis. The X-axis represents log2 fold change (log2FC), and the Y-axis shows the −log10-transformed p-value. The following significance thresholds were applied: p < 0.05 and |log2FC| > 1. Genes meeting both thresholds are highlighted in red; genes passing only the log2FC threshold are shown in green; genes passing only the p-value threshold are shown in blue: a, comparison between M_BIF and M groups (reference); b, comparison between M_LAC and M groups (reference); c, comparison between M_BIF and M_LAC groups (reference). NS, not significant.

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13. Fig. 4. Results of gene set enrichment analysis. Color indicates the normalized enrichment score (NES): a, results obtained by using the MSigDB database; b, results obtained by using the Cell Marker 2.0 database.

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14. Fig. 5. Identification of co-expression modules associated with experimental groups: a, Pearson correlation coefficients between experimental groups and co-expression modules; positive correlations are shown in red gradients, and negative correlations in blue gradients; significant correlations are marked with asterisks; the number of asterisks indicates the level of significance: * p < 0.05; ** p < 0.01; b, normalized expression values of significant modules; the X-axis represents sample identifiers, and the Y-axis represents expression levels. Sample group assignment is indicated by color. Greenyellow, sienna3, pink и royalblue, designations of differentially associated co-expression modules.

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15. Fig. 6. Integrated analysis of co-expression modules associated with group M: a, protein–protein interaction network (STRING) constructed using genes from positively correlated modules. Markov clustering identified functional gene clusters involved in coordinated biological processes; b, gene set enrichment analysis in which the identified STRING clusters were used as custom gene sets. ECM, extracellular matrix; Exp, experimental group; NES, normalized enrichment score.

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